Machine Learning Systems

Harvard University and MIT Press released a free, open-source two-volume textbook on machine learning systems, covering AI engineering from single-machine to fleet-scale infrastructure. The curriculum includes interactive labs, a custom ML framework, hardware deployment kits, and instructor resources, aiming to train one million AI engineers by 2030.

Machine Learning Systems TWO-VOLUME TEXTBOOK Machine Learning Systems. The physics of AI engineering. A rigorous, principles-first treatment of how ML systems are built, optimized, and deployed — from a single machine to fleet-scale infrastructure. Harvard University · MIT Press 2026 Actively maintained Last updated April 2026 Release notes https://github.com/harvard-edge/cs249r book/releases A complete curriculum for AI engineering. Choose a path: read the books, explore trade-offs in labs, build the internals with TinyTorch, model constraints with MLSys·im, deploy on real hardware, practice with StaffML, or adopt the full course with the Blueprint. For Students & Learners labs/ EXPLORE Labs Interactive Marimo notebooks. Change a parameter, see what breaks, build intuition. tinytorch/ BUILD TinyTorch Build your own ML framework from scratch across 20 progressive modules. Zero magic. mlsysim/ MODEL MLSys·im First-principles performance modeling. One command, every bottleneck. kits/ DEPLOY Hardware Kits Deploy ML to Arduino, Seeed, Grove, and Raspberry Pi. Real memory limits, real power budgets. For Career & Instructors staffml/ PRACTICE StaffML Physics-grounded interview questions for ML systems roles. Vault, drills, and mock interviews. instructors/ ADOPT Instructor Hub The AI Engineering Blueprint: two-semester syllabi, pedagogy guide, rubrics, and TA handbook. slides/ TEACH Lecture Slides 35 Beamer decks with speaker notes and 266 original SVG diagrams. Drop in and teach. newsletter/ FOLLOW Newsletter Updates on the curriculum, new chapters, and what the community is building. OUR MISSION AI education should be free and open to everyone. Everyone calls AI the new electricity — but electricity is useless without engineers who can build the grid. For AI to be efficient, reliable, and safe, the world needs engineers who understand how to build it. That knowledge should be accessible to anyone willing to learn. This curriculum is our commitment to making it so. 23,000+ stars · 243,000+ readers · 180+ countries Our goal: 1,000,000 AI engineers by 2030 Next milestone: 100,000 ★ — we're at 23,000+. Every star helps others discover this resource.